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Securing the future of AI

Enabling High-Assurance AI 

Deploy state-of-the-art governed agentic AI across data and IP sensitive workflows

Nestria AI is a deep-tech, research-led company building verification-first AI infrastructure that augments complex expert workflows with deterministic controls, human approval and traceable evidence.

Product

OUR SOLUTION

Hardware, Firmware & Cryptographic Assurance

AI augmentation across RTL, embedded software, side-channel, PQC, lab and product-validation workflows.

Financial & Regulated AI Controls

Multi-agent policy-control architectures through VIF for governed execution, approvals, traceability and evidence.

Sovereign AI for Critical Operations

Secure reference designs deployed on-premises, in private cloud or air-gapped environments with full data and model control.

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Physical & Embodied AI Assurance

Verification and control architectures for robotics, autonomous systems, sensors & AI-enabled hardware from simulation to real-world operation.

Product

NESTRIA AGENTIC PLATFORM

Agentic Platform

Secure, governed agentic AI infrastructure for deploying critical workflows on-premises, in private cloud or air-gapped environments.

Workflow Reference Designs

Reusable architectures combining LLMs, specialised agents, ML/DL models, deterministic tools and customer integrations for specific high-assurance use cases.

Verifiable Inference Framework (VIF)

The embedded control layer that verifies outputs, enforces policy and human-approval gates, and produces traceable evidence - across hardware, firmware, AI software and physical AI integrations

An agentic operating layer for workflows where outputs must remain controlled, reviewable and verifiable.
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 TRUSTWORTHY AI

As enterprises move from experimenting with AI to deploying it across critical workflows, they face a harder challenge: ensuring that agents remain reliable, policy-aligned, traceable, and safe to operate around sensitive data, systems, and intellectual property.

 

Nestria combines high-performance agentic AI with its Verifiable Inference Framework, deterministic tools, human-approval controls, and secure deployment. We build workflow-specific reference designs that integrate LLMs, AI agents, ML/DL models, and customer systems, helping organisations deploy AI with stronger assurance across hardware, financial, regulated, sovereign, and physical-AI environments.

About

ABOUT US

Our Vision

At Nestria AI, we aim to advance reliable automation in the real world

Our Story

Nestria was founded on the belief that AI will increasingly operate across complex enterprise systems, hardware, and the physical world. But this future depends on AI systems that can operate with strong controls, verification, and human oversight. 

 

Led by experts in artificial intelligence, cybersecurity, cryptography, and hardware security, we are building the architecture needed to deploy AI safely in environments where reliability, security, and evidence matter.

Our Technology

Nestria is a secure, model-agnostic agentic AI platform powered by the Verifiable Inference Framework. 

 

We build workflow-specific reference designs combining LLMs, specialised agents, ML/DL models, deterministic tools, policy controls, and customer integrations.

 

These solutions can be deployed on-premises, in private cloud, or in air-gapped environments across hardware assurance, regulated financial systems, sovereign AI, and physical or embodied AI workflows.

Partners

Our Team

  • Built with industry partners around real high-assurance AI and security workflows

  • Backed by Antler, a leading global early-stage venture capital firm

  • Led by AI, cybersecurity, cryptography, and semiconductor-industry experts

Prity Jha

Prity Jha

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Co-Founder

Product & Commercial Leader |

AI / ML Expert |

ex- Google 

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Dr Zakaria Najm

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Co-Founder

Artificial Intelligence, Cybersecurity & Cryptography Expert | 

ex- ST Microelectronics, Continental, Secure-IC

Get a Demo

Request a Demo

See how high-assurance AI can support your critical workflows.

We will help identify a focused workflow, define measurable validation and evidence outputs, and assess the right on-premises, VPC or air-gapped deployment model.

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